The present application relates to the field of X-ray and computed tomography (CT). It finds particular application with security systems configured to image an object and/or to identify potential threat items within an object that is presently or was previously under examination. It also relates to medical, industrials, and/or other applications where identifying sub-objects (e.g., sheet objects or thin objects) within an object under examination would be useful.
Security at airports and in other travel related areas is an important issue given today's sociopolitical climate, as well as other considerations. One technique used to promote travel safety is baggage inspection. Often, a radiation imaging modality is utilized to facilitate baggage screening. For example, a CT system may be used to provide security personnel with two and/or three dimensional views of objects. After viewing images provided by the imaging apparatus, security personnel may make a decision as to whether the baggage is safe to pass through a security check-point or if further (hands-on) inspection is warranted.
To reduce human error associated with identifying potential threat items inside the baggage, automated object recognition systems may be utilized. Such systems can extract a sub-object from an image, and compute properties of the sub-object based upon properties/elements of the image or representation of the sub-object. Computed properties of the sub-object can then be used for discriminating a sub-object by comparing the sub-object's properties (e.g., density, effective atomic number, shape, etc.) with known properties of threat items, non-threat items, and/or both classes of items, etc.
To accurately compute a sub-object's properties, a representation of the sub-object is typically segmented from portions of the image representative of other sub-objects. Algorithms utilized to segment the image (e.g., to isolate sub-objects in the image) may depend upon, among other things, the size and/or shape of the sub-object. Accordingly, algorithms utilized to segment sheet objects (e.g., also referred to as thin objects) may be different than algorithms utilized to segment bulk (e.g., non-thin) objects.
U.S. Pat. No. 6,111,974, assigned to Analogic Corporation and incorporated herein by reference, describes one approach for segmenting a sheet object from other sub-objects represented in an image, such as by identifying sheet voxels, for example. While such technique have proven effective, surfaces of some bulk objects may be difficult to distinguish from sheet objects. As such, voxels associated with surfaces of bulk objects may sometimes be mislabeled as voxels associated with a sheet (e.g., and trigger a false alarm during threat detection).